Night-Time Light Remote Sensing Mapping: Construction and Analysis of Ethnic Minority Development Index

Zhao, Fei; Song, Lu; Peng, Zhiyan; Yang, Jianqin Jenny; Luan, Guize; Chu, Chen; Ding, Jieyu; Feng, Siwen; Jing, Yuhang; Xie, Zhiqiang

doi:10.3390/rs13112129

Cited by 50 publications

(17 citation statements)

References 42 publications

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“…To make the best use of land resources, obtaining information on land-use potential seems to be necessary [2]. Recently, multispectral imagery and remote sensing technology, which provide a better understanding of the Earth environment's different dimensions [3][4][5][6][7], have emerged as important tools to study land use/land change (LU/LC) and to assess its potential [3,8,9]. Amid various changes in land use, urbanization has changed the natural appearance of the Earth's surface by introducing new land uses and coverings.…”

Section: Introductionmentioning

confidence: 99%

Investigation and Prediction of the Land Use/Land Cover (LU/LC) and Land Surface Temperature (LST) Changes for Mashhad City in Iran during 1990–2030

et al. 2023

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Studies on how cities are affected by urban heat islands (UHI) are critical nowadays for a better understanding of the connected effects and for providing helpful insights for sustainable city development planning. In this study, Landsat-5 Thematic Mapper (TM), Landsat-7 Enhanced Thematic Mapper+ (ETM+), and Landsat-8 Operational Land Imager (OLI) images were used to assess the dynamics of the spatiotemporal pattern of land use/land cover (LU/LC) and land surface temperature (LST) in the metropolitan city of Mashhad, Iran in the period between 1990 and 2019. The Markov chain model (MCM) was used to predict LU/LC and LST for 2030. In the analyzed LU/LC maps, three LU/LC classes were distinguished, including built-up land (BUL), vegetated land (VL), and bare land (BL) using the maximum likelihood (ML) classification method. The collected data showed different variations in the geographical pattern of Mashhad LST during the research period that impacted the LST in this metropolis. The study evaluated the variations in LU/LC classes and evaluated their impact on the LST. The value of the LST was positively correlated with the occurrence of the built-up land (BUL), and with the bare land areas, while it was negatively correlated with the occurrence of the VL areas. The analysis of changes observed over three decades with 10-year intervals and the prediction of the LU/LC and LST for 2030 constitute an important contribution to the delineation of the dynamics of long LU/LC and LST records. These innovative results may have an important impact on policymaking fostering environmental sustainability, such as the control and management of urban expansion of Mashhad in connection with UHI.

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Section: Introductionmentioning

confidence: 99%

Investigation and Prediction of the Land Use/Land Cover (LU/LC) and Land Surface Temperature (LST) Changes for Mashhad City in Iran during 1990–2030

et al. 2023

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“…Advances in remote sensing and geographic information system (RS-GIS) technology (Chao et al, 2021;Li et al, 2022;Wang, Zhang, et al, 2021;Wang, Fang, et al, 2021;Yin, Wang, Keim, et al, 2022;Zhang et al, , 2023Zhao et al, 2021), along with statistical and artificial intelligence models (Li et al, 2021;Meng et al, 2019;Wang, Zhao, et al, 2022;Wang, Henning, et al, 2022;Xie et al, 2021), greatly help in the mapping of a number of natural hazards with proper management of environmental issues through land use planning. Studies of flood hazards incorporate geospatial technologies, particularly GIS and RS techniques.…”

Section: Introductionmentioning

confidence: 99%

Flood hazard potential evaluation using decision tree state‐of‐the‐art models

et al. 2023

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Floods occur frequently in Romania and throughout the world and are one of the most devastating natural disasters that impact people's lives. Therefore, in order to reduce the potential damages, an accurate identification of surfaces susceptible to flood phenomena is mandatory. In this regard, the quantitative calculation of flood susceptibility has become a very popular practice in the scientific research. With the development of modern computerized methods such as geographic information system and machine learning models, and as a result of the possibility of combining them, the determination of areas susceptible to floods has become increasingly accurate, and the algorithms used are increasingly varied. Some of the most used and highly accurate machine learning algorithms are the decision tree models. Therefore, in the present study focusing on flood susceptibility zonation mapping in the Trotus River basin, the following algorithms were applied: forest by penalizing attribute—weights of evidence (forest‐PA‐WOE), best first decision tree—WOE, alternating decision tree—WOE, and logistic regression—WOE. The best performant, characterized by a maximum accuracy of 0.981, proved to be forest‐PA‐WOE, whereas in terms of flood exposure, an area of over 16.22% of the Trotus basin is exposed to high and very high floods susceptibility. The performances applied models in the present work are higher than the models applied in the previous studies in the same study area. Moreover, it should be noted that the accuracy of the models is similar with the accuracies of the decision tree models achieved in the studies focused on other areas across the world. Therefore, we can state that the models applied in the present research can be successfully used in by the researchers in other case studies. The findings of this research may substantially map the flood risk areas and further aid watershed managers in limiting and remediating flood damage in the data‐scarce regions. Moreover, the results of this study can be a very useful for the hazard management and planning authorities.

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“…e other side shows the high-security requirements and needs for data communication in the cloud computing environment [26,27]. For the better use of distributed computing, there is a need for productive treatment of the assets and there ought to be components to give a type of positioning and procuring the system safely.…”

Section: Introductionmentioning

confidence: 99%

An Empirical Client Cloud Environment to Secure Data Communication with Alert Protocol

Soni

Srivastava

Bhatt³

et al. 2022

Mathematical Problems in Engineering

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Cloud computing is a technology that provides resources and utility services based on user demand. Due to this demand, efficient cloud security protocols are highly required, especially at the time of data communication for user authentication and data aggregation. The data communication scenarios are majorly affected by the security threats in the cloud computing environment. This article provides a practical approach to developing an efficient and empirical cloud framework in terms of cloud protocol. The framework uses fuzzy c-means (FCM) algorithm to group data, and calculation is done individually or associatively to rank the text data. Uploaded data are passed to a simple additive weighting (SAW) algorithm for ranking and making decision selection. The framework executes in three phases, namely data preprocessing, clustering, and automatic data security with an alert mechanism. The process is completely automated so there is no need of considering the individual files for the processing and the data held will be appropriately correlated with the sharing inter-cloud environment. To inspect security issues, the proposed framework is secured by three different security algorithms. The encryption process is completed by Rivest Cipher 6 (RC6); the substitution process is done by Advanced Encryption Standard (AES); and key generation is done by RC6, AES, and Rivest-Shamir-Adleman (RSA) approaches collectively. Based on the given situations, these standard approaches were automatically applied separately or collectively. Unauthorized access trapping and data deletion mechanism are also provided in the proposed framework. The experimental results with a comparative study depicted the effectiveness of the proposed work.

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Night-Time Light Remote Sensing Mapping: Construction and Analysis of Ethnic Minority Development Index

Cited by 50 publications

References 42 publications

Investigation and Prediction of the Land Use/Land Cover (LU/LC) and Land Surface Temperature (LST) Changes for Mashhad City in Iran during 1990–2030

Investigation and Prediction of the Land Use/Land Cover (LU/LC) and Land Surface Temperature (LST) Changes for Mashhad City in Iran during 1990–2030

Flood hazard potential evaluation using decision tree state‐of‐the‐art models

An Empirical Client Cloud Environment to Secure Data Communication with Alert Protocol

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